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Supply Chain Forecasting with Advanced Analytics · Speaking of Supply Chain

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You're listening to Speaking of Supply Chain, a Mebach podcast. This is a
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show for logistics professionals looking to learn more about the latest innovations in supply chain.
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Each episode will feature a conversation on topics such as mitigating supply chain disruption
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and reducing risk, current automation trends, sustainability initiatives, and more. Let's
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dive right in. Hello, and welcome to Speaking of Supply Chain, where
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we explore trends, current events, and innovations impacting the supply chain in logistics
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industries. I'm your host, Ellen Wood. So today we're going to be
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talking about forecasting and if using external data helps or if it overcomplicates an already
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complicated issue. And with me today i have Jillian Verse drafting from Soul Venture.
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Welcome to the podcast. It's great to have you. Thanks, I'm
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very honored to be here. Thank you very much for the invitation. And
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we're also going to be talking with Gustavo Escobar. He is with me back
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and it's it's great to have you on our podcast. Gustavo, I'm looking
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forward to having our conversation today, which is forecasting and demand planning and so
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what is what is soul Venture's role in this? Tell me a little bit
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about the company. So Venture offers a supply chain, tooling and services for
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around the whole SNOP process. And so I mainly focus my career at demand
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planning and I've been working on that already for a couple of years now.
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Also did a PhD on the topic of forecasting. So so it's a tool,
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it's a software. Well, if we're talking about leading indicator forecasting,
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it's indeed a tool and a service also that we offer to clients. And
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so it's not only the tool, but also how to interpret the tool and
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how to make most of fits. So I mean demand is going up and
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down all all over the place. You know, I know here in the
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US at least, you know, all these container ships that have been stuck
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out in the ports, you know, waiting to be able to get in
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our you know, all of that backlog is finally cleared. Stuff as hitting
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shelves that people had ordered or businesses had ordered for retail purposes months and months
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ago, or supplies that they had ordered you know, a year ago,
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are finally arriving there like, well, I don't need it now, We've
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changed our plans. So given that demand planning can be kind of at the
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mercy of a lot of external things. Um, what does that mean now
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that we're kind of mirroring the end of some of those peaks and valleys.
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So maybe it's also a good idea to take a step back, do you
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exactly predict demand rights? So typically there's a sales FORECST process that's thanks to
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use statistical techniques and so those typic we extrapolation techniques like all twinters and so
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on, those extrapolates historical trends and seasonal patterns. Next to that, typically
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in the process of demand planning, there's adjustment being done by the sales team
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or other stakeholders in that demand planning process. So indeed, as you say,
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there's been massive backlogs over the past year of goods that were ordered a
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year ago. Also headed myself, I ordered think my car in October twenty
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one. I only got it in August twenty two, so it definitely took
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almost a year to get that car to me. So yeah, that's such
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unprecedented times. Make it just essential for to not only look at those extrapolation
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techniques and to not only rely on salespeople to adjust those forecasts, but also
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to include external data. And that's the mount planning process. I would like
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to add their ELM because you mentioned a good point like what is happening right
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now right with the industrials and everything after we're going down with this kind of
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like pick that we were having right so basically we were seeing within our customers
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that once that we that we bring a couple of support. Is that all
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of this planning process that was happening, and let's talk about it. For
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example e commerce, right, we plan in advance, like hey, e
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commerce is going to be exploding because it is going up, going up,
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going up. And what's what happened right now? These companies are having like
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a huge portion of inventory work, working capital, inventory costs because this is
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still I mean, we're holding this amount of like total units or pounds or
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whatever that we're holding inside of our disease just because we plan in advance with
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this kind of light techniques. Right. But as Julian is mentioning, I
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guess we need to in a certain way, we need to find options to
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kind of determining when this is going to be happening, how this is going
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to be happening, but also how to react to this, right, because
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it's not just detecting it, it is also like understand how we're going to
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be taking decisions out of it exactly, and to build on top of that.
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I think a lot of companies got away with only using traditional techniques before
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covids, but you have to imagine what all the events that happened over the
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past three years. It's crazy to look back at it, right. So
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we've had multiple COVID lockdowns, we've had a new normal trend, and e
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commerce got a lot bigger. As you mentioned with stuff. There's been supply
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issues like you've mentioned allan, there's shipping costs that have exploded over the course
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of twenty one. Indeed, we're at war also in Ukraine, and there's
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been a war occurring that's always also took It has been years since the war
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has been impacting as I think, there's been a related energy crisis here in
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Europe also with related inflation on top of that, and then now we're actually
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seeing also a different rent Again. Last year it was all about inflation and
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supply shortages, whereas nowadays it's more a shift from being supply constrained to actually
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being demand constrained where you see that. Okay, companies were having or perceiving
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that demand was enormous, but came to to the conclusion that now the supply
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issues were or over that a large part of that demand is actually has been
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artificial, so to speak. Okay, now I know that in you know,
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an example that we had here that we were talking about in the office
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was, you know, all of the product and ski rationalization that happened in
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a lot of FMCG companies when they couldn't get materials in order to package their
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products that they would normally be producing. I remember someone was at the grocery
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store and the distributor was their stocking shelves and she was like, oh my
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gosh, you have this product again. What happened? And he was like,
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yeah, we couldn't get the packaging. It was you know, tied
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up. And so a lot of that is starting to even out and they're
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able to offer products again. But all of that demand that had built up
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where people were saying, where's my product, where's my product? But they
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only needed maybe one or they they didn't need it as much as as they
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thought they did. And you know, they found something else in the meantime,
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So you know from the consumer side that's going to drive a lot of
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it as well, and how how are companies going to plan for that?
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How are they going to identify that whether the demand is real or whether it's
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not real. And so that's where I at least I understand. So le
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Venture comes in and starts to look at some of that data and say,
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Okay, is this is this accurate? Is this real? Is this this
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external data? Is this useful at this point or is this old information?
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Is this old news? And what does it mean? Yeah? Exactly.
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It's also something that we have been doing or a project that we have been
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doing with the client in the chemical business that was creating polymers. Let's go
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into pars. So what we see. What we saw there is let me
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take a step back. Also, if you're in retail or in FMCG,
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you're still so close to the end consumer. But if you're a chemical company
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that has maybe five or six steps to the end consumer, then you're really
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steering blind if you're not, if you're not including any external data. So
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what we did there also it's built for each of those steps in their value
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chain downstream and so it goes an automotive. So what what what what do
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you have? You have your sales to consumers, and that's vehicle sales.
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That's something that you can monitor. You have vehicle production that you can monitor,
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you can product monitor the production of parts. That's all very valuable information
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that a company that is so far upstreme. If you're not monitoring that and
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you're only listening to what your what your own clients are seeing, it's really
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steering blind. And it's could have gotten away with it before COVID, I
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guess, but ever since all those disruptions starting started occurring, it's it's becoming
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increasingly difficult. And I guess that's a valuable point because to me consumer and
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understanding the consumer in the first point, and understanding the demograph, how are
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they behaving right? How is they spend for them going to be behaving in
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the future, How, for example, even the real estate the high interest
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rates that are happening right now for example all around the world, how this
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is going to be spending the whole amount of money that they can't spend in
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the products for the companies, right, So we need to be closer to
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understand, like, hey, how this is behaving. How we can take
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it on time and with reliable data, because as Dan was mentioning, probably
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we can take the data, but this could be like four months old and
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this is not going to be working at all for us. But how do
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we capture it, how it is going to be reliable, and how we
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take this inputs specifically to combine them with the for example, the historical data
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or the trends that we were having inside of the company, together with other
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external factors that probably we can discuss late it too. All right, So
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Jillian, you were mentioning about the chemical companies and how they might be six
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steps removed from the end consumer. Don't we take into account those interim consumers
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in terms of their demands and so those those steps in between. Doesn't that
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count as an internal customer or an internal supplier? I mean, isn't that
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that data worth something? Does it have to trickle all the way down to
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the end consumer to be valid? No, of course, it's a good
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point to make, even it's it's it's very valuable information I think to monitor
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upon The point isn't trying to make is that if you're in a supply constraints
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environments and your clients or basically begging you to deliver your goods to you.
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How do you expect your clients to order? They will for sure inflate their
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orders so that they will have enough goods poppy for themselves. So in that
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sense, if you're in a supply constraint environment, it might be somewhat less
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valuable to to to monitor that information, but of course it's it's still valuable.
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And if you're able to monitor all those interim chains, it will give
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you so much visibility and you would see the demand flowing first from the end
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consumer, then to the first chain, all the way up to you your
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own demands. Right, that's all talking about you know, internal data data
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that companies typically have access too. But one of sol ventures, you know,
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key aspects is looking at external data. So what does that even mean?
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What does that consist of? What is external data that's relevant for supply
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chains. Yeah, there's a whole lot of external data available in various types
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and formats, And so where I work in most of the time is regarding
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macroeconomic indicators, and so think of how production at a certain stage or certain
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part of the economy, or for example, oil prices or various other macroeconomic
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indicators how they will impact your sales. But next to that you also have,
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for example, weather information. There's a whole lot of companies that get
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very high demand if the weather is in a certain type of format. There's
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also parts of external events. I think Chinese New Year has recently past that
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also is a high impact for companies for consumers also, strikes, for example,
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can legislation have a huge impact on demand. So how long is the
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cycle that people are or that companies are typically looking at when they're looking at
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this external data, because some of the things you mentioned are long term and
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some could be very short term, like you know, Chinese New Year.
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Okay, you can kind of plan for that, but if there's a labor
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strike, you need to know that now because it could be over by the
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time you've corrected anything. Yeah, exactly. So it's actually two folded if
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you're you're thinking about it, and so first, it's historically about cleaning your
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data. You need to know what has happened in the past to be able
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to filter out for the future and so that your forecast can can work on
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an clean time series. And in the future, it's of course, depending
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a little bit on how you how long you know your indicator behavior beforehand.
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Right, don't ask me to predict what the weather will be within one month,
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because I for sure I don't know how it's what it will be.
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I hope it's good, but who knows. Same with strikes. But once
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you have your impact of how those indicators or data can impact your sales or
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your company, you can actually take that into account for your planning processes.
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If you know, okay, the effect of good Twitter in March has that
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effects, then you might be able to mitigate for those impacts in the future.
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For instance, if a particular industry looks like it's going to have a
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strike, like the airline strike that happened a few years ago, if you
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know that that's going to happen or you foresee that as a possibility, you
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can use that data to help adjust what you may have to do or the
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ways you may have to decide in the future. Even though that's kind of
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a last minute decision. You can use that data to plan what plan B
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plan see all the way to queue if you need to, Yeah, and
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I was going to add like to me, it's like, for example,
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not even knowing when it's going to be happening, because as Dealing was mentioning,
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we don't have like this crystal ball, right. I mean, no
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one is going to be a magician and tell you the hey, in two
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months, there's going to be happening. But at least to me, let's
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say that we are going to be creating this kind of mechanisians like for example,
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prescriptive tools, right that could tell us like, hey, when this
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happens, how we should be reacting and which kind of decisions we're going to
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be taking, right, Because to me, it's like not even planning,
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but actually planning to be covered to minimize the risks now we're going to be
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having with any kind of like potential disruptions that we're going to be facing in
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the future too. So it is like and also to me, it's important
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to add like we don't need to move away, for example, from the
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descriptive tools, like for example, the past information that we have that's always
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going to be happening happening for us, and we also need to take them
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into account. But we need to find like a layer or like layer this
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kind of information like the external one with our internal one because otherwise, I
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mean we don't align the two perspectives, we're not going to be going anywhere
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too well. And Jillian, another point that you made was cleaning the data.
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So and I know stop as some probably horror stories about having to clean
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data. How many companies are truly ready with clean data and how many need
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to take a step back and really dig through it first. It's a difficult
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question to all to answer. Actually well, from my experience, a lot
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of companies are not as skillful as they should be on that kind of topic.
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And you know what is the problem. I guess the companies believe that
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they are ready, but once they jump into in and that's what I faced
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actually with the customers. Once we jump into the data and everything, there's
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nothing there or at least information needed to go on create these kind of tools
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like predictive or prescriptive. None of them are actually like in the world no
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word shape. So all of these things, the data, the issues we
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like it, even issues that maybe the customer is not going to be ready
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for that sometimes it's not fully aligne And I would say that I was reading
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an article also like recently that you usually like just fifteen percent of the companies
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of like in the meter to two big sized companies are actually ready to use
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this kind of like tools. By the way that they are actually ready to
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do it was that fifteen as in one five. So that's that's scary if
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you're one of those companies out there to think that, oh my gosh,
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I might not be ready for this because I don't have the data that I
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need to make this happen. And sometimes it is they could have the data,
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but they don't even know what to do. That's the portion that we
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well, that's where you guys tell me, that's where we come here and
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support the customers too. To build on top of your response, Gustavo,
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So, I think we did a survey a couple of months back with companies
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or whether they already use external data. The responses we got was sixty percent
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still doesn't use external data and therefore costing processes. So it's crazy to think
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about it in current times actually correct, and sometimes that it's not even a
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consideration, right exactly, and sometimes they just consider like the external data that
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probably impacts their industry, but none nothing else compared to what they could be
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having, like in an overall like landscape, for example, So they're just
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focusing on their niche, but they should be focusing on like in a higher
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perspective. So they may be focusing on the same type of external data like
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shipping rates yea, and not necessarily the cost of wheat being higher because of
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the war, in correct, you know these types of things or sunflower I
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think sunflowers was one of the products that are that's grown in that region,
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and sunflower oil was something that was was going to be affected because of it.
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So, you know, these things that they may not have thought of
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because they're so focused on the one piece of external data they are thinking about
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that they're not looking at the other ones. That is correct, all right,
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So what can companies do to start paying attention other than hiring the two
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of you to to help them, you know, and tell them what they
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need to do. What types of things should companies be looking for when they're
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they're looking at their you know, their business planning for the next couple of
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years. There's no other option than to hire us, of course. Well,
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of course that's why we're here now. I was going to say,
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I mean, the companies, for sure, apart from hiring us, they
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will need to first understand their business right. Sometimes we will live that they're
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actually understanding the fully like the fully like a new one sense of their business
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that they're having. But to me, that's just should be like the first
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step, where do they want to go? Where is the business going to?
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Who are our customers? Who do we want to serve? Right?
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So understanding that mission vision that they're having it will be crucial for them because
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then we will need to react and understanding what are the external factors that are
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going to be impacted that business is strategy that they are going to be having
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right. Second one to me, they will need to understand, as we
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were mentioning, understanding the real customers. That is tied to the first topic
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that I was mentioned too, and they are to me, they will need
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to start putting together a couple of like internal activities to understand what they are
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actually having inside of the data right to see and to have like an assessment
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like hey, what do we have if we don't have it, how we
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are going to be closing it? Right? And what we would like to
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measure because we can measure thousands of like KPIs and metrics and everything, but
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if every percent of them are not even like aligned with our business strategy,
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then why do we actually look at them? Yeah, why are we capturing
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that data? It's not relevant, it's not used correct. So we need
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to first like understand what is happening from the data. Second, build this
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either predictive for example, tools that can tell us like, hey, when
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something is going to be happening, but yet we don't know how we're going
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to be reacting to it. Right. And once we close those gaps and
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find like for example, like closing the gaps with the technology, with people,
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with any kind of stuff that we could bring to to understand like the
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data that we're having, then we need to build together like a couple of
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these prescriptive tools that will help the decision making for all of the leaders inside
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of the organization, right because at least they will be having a couple of
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options. Let's say that while we are facing with a couple of customers,
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with some a couple of prescriptive tools, they can already know how the inventory,
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if something like the COVID happened, how the inventory could be allocating unoptimized
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inside of the organization. Right. If some of these main parameters are are
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going to be changing right, So they can already take a couple of decisions
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based on this information that they're going to be receiving, but they will need
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to be able to at least be able to react and to understand the operation
23:07
that they're having. So it's like a process they need to evolve in a
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certain way and they need to match, I mean, to get more matured
23:15
in the process of being like able to have like a more solid, for
23:19
example, forecast accuracy. Too often, I've also noticed that companies they do
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have some kind of information available typically that's then concentrated into the marketing department,
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for example, where the strategic marketing department uses that external data to create a
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strategic outlook on how the business is evolving. Well, that's also useful information
23:47
for creating your sales forecast for your tactical demand planning process, of course,
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if you can follow up on how that data is evolving in real time and
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extrapolate that to the future. So typically it's also a part of cooperating with
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the different divisions and the demand planning process and expanding the view of what you're
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doing in the planning process. So do you find that any industry is particularly
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better at these things than other industries. Is there an industry that desperately needs
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help and needs to update? Is it? Does it depend on the maturity
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of the company itself as opposed to necessarily the industry. What are some of
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the things that are that are indicators or or that would help you identify which
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potential contacts or potential companies need your help the most. Yeah, it's a
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good question. So what we what we typically see is that it works best
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with cyclical companies, and so I think automotive for construction companies that have huge
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swings up and down as the economy moves up and down, as well as
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companies that are further upstream and the value chain. Right, So the further
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you're upstream, typically the longer the leading value is of the indicators that we
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find. So if you're, for example, that chemical player, we can
25:21
find or refund for them indicators that have up to six months eating value.
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So that gives you insights on how the next six months of your sales can
25:32
evolve based on the end market. Right, it will be different pain points,
25:37
I guess as you're mentioning getting like, depending on where you are in
25:40
the tunnel supply chain, you will need to react in a different way versus
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if you are like upstream or downstream or example, right, like the retail
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ones there will be knowing their customers as you were mentioning, right, there
25:53
will be knowing the real customer of it. But an upstream like the suppliers
25:59
that they're having, maybe they don't have that disability. And that's where we
26:02
start having like this bullwhip effect, right, like they start creating inventor and
26:07
inventor and inventory upstream and creating additional costs and disruption and issues inside of leg
26:14
supplation toil. Yeah, and in fact, Jelie, you had said something
26:18
similar to that in a previous conversation where you were saying that companies that do
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use the external data or don't use the external data apologize. Companies that don't
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use it are are driving blindly into a storm or the words that you used,
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and you said you have examples share share some examples. Well, it's
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another company that we've worked with. But for example, there was a big
26:45
retailer of second Hunt vehicles in the United States. Um that was of course
26:52
one of the booming businesses. When COVID hits right, there were long waiting
26:59
times to get a new car, and so what do you have to do
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if your car breaks down? You have to buy a second hand car.
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Then you can't way the year for your new car to arrive, and so
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that drove indicators upward. And so what we've done also there is monitored consumer
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prices for used vehicles that one shot up up to fifty percent between twenty one.
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It's crazy, right, the same vehicle all of a sudden it's fifty
27:27
percent more expensive. But of course the good times there didn't last, and
27:33
as the lead times got considerably shorter, the demand for those used vehicles went
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down, and you're basically getting hit from all the sides then, right,
27:45
lower volumes, lower sales value, lower profits. And you also have to
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take into accounts all the inventories that you're having, you have boughted at a
27:56
certain price, so you need to make sure that you can still set it
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with profits. So you might have a serious margins queeze also occurring there as
28:07
consumer prices to normalize. I believe I know which which company you're talking about,
28:12
and I recognized because there were there were a number of their locations here
28:18
in Indianapolis and they are no longer there, and that's sad because we don't
28:26
want that to happen to businesses. We want companies to be successful. So
28:30
automotive is one. What is and that's a bad example or an example of
28:34
where it really hit them hard. What's an example of a customer who was
28:40
able to succeed with this information? Well, with the polymer company that we
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have worked with, we have predicted their sales and using a sales prediction model.
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That model included vehicle sales and if you monitor that, it's also linked
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with the example I just gave. But there you really saw the impact of
29:06
that supply chain shortage shortage, right, So what happened is that's I think
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it was during the course of April of twenty one, all of a sudden,
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the vehicle sales went down, the new vehicle sales because of that semiconductor
29:26
shortage. So we found that that indicator had a couple of months leading value
29:30
on their sales, so they were able to see that turning point coming up
29:36
in ahead of time and being able to adapt to changes that were occurring in
29:41
the demand. So that's a huge competitive advantage if you have kind of an
29:48
early warning signal that business is changing, that there might be potential headwines or
29:55
tail wines coming up. It can add tremendous value, and I guess the
30:02
being able to actually determine these cute quick signals as you will mentioning Jillian,
30:08
it is like the I mean, for sure it would be a competitive factor
30:14
as you mentioned, right, but I guess it will be able for I
30:17
mean it would provide for them like our resource to a both quicker instead of
30:23
like the company, right, And it's going to be for sure like creating
30:27
like this is kind of a snowball effect where they are going to be ready
30:32
for even upcoming events that could be impacting in a harder way. But if
30:37
they were already like ready for these other kind of like kind of disruptions in
30:42
the beginning, then they will be ready for even like even if there are
30:47
a couple of deviations in the things that are going to be happening, the
30:49
level could be like the same thing, right, but at least they will
30:52
be having a plan and an strategy for or guidelines on how to react to
30:56
it. So is the adience for this message is that only the C suite
31:03
do the supply chain, you know, managers and professionals obviously they recognize the
31:08
importance of this, but it feels like a lot of these decisions are made
31:12
from that C suite, from the CFO, from the CEO in terms of
31:15
the way they're driving the business and trying to move that business forward, and
31:22
that they're the ones that need this information, not just the supply chain guile
31:25
though it's probably the supply chain person going hey, is there is there someone
31:32
in particular that we should be spreading this message to or is it just kind
31:37
of everyone who has a stake in moving that business to that next step?
31:45
This for sure should be impacting like the C level people inside of the organization.
31:48
But to me, it's more not like the I mean, they for
31:51
sure will be the ones taking the decisions. They will be for sure the
31:53
ones stealing like the wheel for doing all of these kind of things. But
31:57
at least from them, it should be the message. It should be like,
32:01
hey, delivering like the importance of how this is going to be impact
32:06
in the business and sending that message that downwards into into the company. Right,
32:12
because if this is not coming from from from this kind of level of
32:15
people, then for should the other ones will not be paying attention to it.
32:20
But for should the other ones that are below should also like being line
32:24
in terms of like, hey, what's going to be the effort, what
32:27
it's going to be like the for example, the time require the act requirements
32:31
for it, because yeah, that they would like to have this, but
32:35
then we have this right. So to me, it should be coming from
32:38
all the kind of the levels of the company, but the message should be
32:42
coming from from the CELC level people too, Yeah exactly, and I'll built
32:46
on that. What we typically see it's the P and L or the business
32:50
owner that is interested in these kinds of techniques and they have to to to
32:55
or they have responsibility to the C level typically, so they want to know
33:01
with more certainty where for the next couple of months their business is going to
33:06
evolve, what is realistic, where are they going to end up? So
33:10
these techniques there also for that level. It adds a lot of visibility in
33:16
the process. Awesome. So any last thoughts as to what you might want
33:22
to say to the listeners out there in terms of, you know, the
33:27
importance of business planning and understanding all of the factors that go into that forecasting
33:31
model. If you're demand planner, it's very important to not only focus on
33:37
what's happening inside the company, but also what's happening outside the company, was
33:43
monitor how the demands is evolving and inventory levels and all kinds of constraints are
33:49
evolving in your value chain. It don't need to start very complex. You
33:57
can start quite easily. We're just picking some proxy indicators that replicate, for
34:05
example, the behavior what's occurring downstream. And if you're monitoring that, you're
34:09
already steering a lot less blinds. Then if you were not to do that,
34:15
and to me, I would say that we should be. I mean
34:19
as a final message that you should be switching our mindset from being let's say
34:25
reactive, right to be more proactive into like, for example, building already
34:34
like the future that we would like to have. Right, it is like
34:37
we have like the ways we have, like the techniques, we have,
34:39
the data already there external data too, right, So why we don't do
34:44
it? Why why we don't go ahead and capture all of this nuance is
34:49
happening in the market and combine it and be ready mitigate risk, mitigate cost
34:55
right inside of the organization. So it is all benefits associated to it.
35:00
Now we have like all of the things ready for us. So we just
35:04
need to be prepared. We need to be, for example, aware of
35:07
that dis is existing, because if we're not even aware, then we're in
35:10
an even earlier stage. But at least this for sure will be having like
35:15
a massive, massive impact and how the business is going to be behaving.
35:21
I'm for sure like in success of all of those ones too. Well.
35:24
The good news is that now all of our listeners are aware, so they've
35:28
they've taken that first step. Now they know, they know that they need
35:31
to know, and they know what they need to do to know more.
35:37
So thank you so much Gustavo and Jillian for joining me today to talk about
35:43
this. I appreciate your time and I hope you'll come back sometime, let's
35:46
hope, So thank you so much for the time. So that brings us
35:51
to the end of our episode. I'd like to give a quick shout out
35:53
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